A multivariate empirical wavelet transform approach for patient-specific EEG seizure detection achieved an average accuracy of 99.41%, sensitivity of 97.91%, and specificity of 99.57%.
Does a multivariate empirical wavelet transform approach improve patient-specific EEG seizure detection compared to state-of-the-art methods?
A multivariate empirical wavelet transform approach provides highly accurate patient-specific EEG seizure detection.
OBJECTIVE: This paper investigates the multivariate oscillatory nature of electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure detection. METHODS: The empirical wavelet transform (EWT) has been explored for the multivariate signals in order to determine the joint instantaneous amplitudes and frequencies in signal adaptive frequency scales. The proposed multivariate extension of EWT has been studied on multivariate multicomponent synthetic signal, as well as on multivariate EEG signals of Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG database. In a moving-window-based analysis, 2-s-duration multivariate EEG signal epochs containing five automatically selected channels have been decomposed and three features have been extracted from each 1-s part of the 2-s-duration joint instantaneous amplitudes of multivariate EEG signals. The extracted features from each oscillatory level have been processed using a proposed feature processing step and joint features have been computed in order to achieve better discrimination of seizure and seizure-free EEG signal epochs. RESULTS: The proposed detection method has been evaluated over 177 h of EEG records using six classifiers. We have achieved average sensitivity, specificity, and accuracy values as 97.91%, 99.57%, and 99.41%, respectively, using tenfold cross-validation method, which are higher than the compared state of art methods studied on this database. CONCLUSION: Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings. SIGNIFICANCE: The proposed method develops time-frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.
Bhattacharyya et al. (Mon,) conducted a other in Epileptic seizure. Multivariate empirical wavelet transform (EWT) vs. State of the art methods was evaluated on Seizure detection accuracy. A multivariate empirical wavelet transform approach for patient-specific EEG seizure detection achieved an average accuracy of 99.41%, sensitivity of 97.91%, and specificity of 99.57%.